BUSINESS RESEARCH

Problem Solving

Organisations often face complex problems that cannot be solved through intuition alone. Structured problem solving provides a systematic approach to identifying issues, analysing their causes, and implementing effective solutions. By applying analytical thinking and evidence-based reasoning, professionals can move beyond assumptions and address underlying challenges. This Hot Topic explores the key stages of problem solving and how a structured approach improves decision-making and organisation

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Problem Solving

Effective problem solving rarely happens by chance. In organisational contexts, addressing complex challenges requires a structured approach that enables individuals and teams to understand the situation, analyse contributing factors, and implement effective solutions. Research on decision-making highlights that systematic processes improve the quality of decisions by reducing reliance on assumptions and encouraging analytical thinking (Simon, 1977).

The first stage of problem solving is defining the problem clearly. Many organisational issues are initially recognised through symptoms, such as declining performance, delays in delivery, or customer dissatisfaction. However, these indicators do not always represent the underlying problem itself. Careful definition of the issue helps ensure that attention is directed toward the actual challenge rather than its effects. This stage often involves gathering initial information, understanding the context of the issue, and identifying the stakeholders affected by it.

Once the problem has been defined, the next stage involves analysing the situation in greater depth. This typically includes reviewing available data, examining relevant processes, and identifying patterns or contributing factors that may influence the issue. Analytical investigation helps organisations move beyond assumptions and develop a clearer understanding of the conditions surrounding the problem.

Following analysis, individuals and teams move to generating possible solutions. At this stage, different approaches to addressing the problem are explored and evaluated. Structured problem-solving models emphasise the importance of considering multiple alternatives rather than immediately selecting the first solution that appears viable. Kepner and Tregoe (1981) highlight that evaluating alternative options improves the likelihood of identifying solutions that address the root causes of a problem rather than simply alleviating its symptoms.

Once potential solutions have been considered, the next stage is selecting and implementing the most appropriate course of action. This requires evaluating the feasibility, risks, and expected outcomes of the available options. In many organisations, this stage involves collaboration between different teams or stakeholders to ensure that the proposed solution aligns with operational requirements and organisational priorities.

The final stage of the problem-solving process is evaluating the outcome and learning from the results. After a solution has been implemented, it is important to assess whether the intended objectives have been achieved. This may involve reviewing performance indicators, analysing new data, or gathering feedback from stakeholders. Continuous evaluation enables organisations to refine their approaches, identify lessons learned, and prevent similar problems from occurring in the future.

In modern organisations, structured problem solving is increasingly supported by data and analytical tools. Davenport and Harris (2007) emphasise that organisations capable of analysing data effectively are better positioned to understand complex challenges and improve decision-making. Similarly, Provost and Fawcett (2013) highlight that data-analytic thinking helps professionals identify patterns, test assumptions, and evaluate the impact of different solutions.

Structured problem solving encourages organisations to approach challenges methodically, ensuring that problems are properly understood before solutions are implemented. By analysing situations carefully, evaluating alternative approaches, and reviewing outcomes, professionals can develop solutions that address underlying causes rather than temporary symptoms.

Referenced techniques

Technique

Critical Path Analysis

Helps decision-makers understand CPM through a concise overview of the technique, its benefits, and implementation. Recent research shows schedule risk stems from the whole activity network and from duration variability, so pair CPM with variability-aware checks (Vazquez et al., 2023; Hasan and Lu, 2024).

Technique

Exploratory Analysis

Exploratory Data Analysis (EDA) is a critical stage in the data analysis process, enabling analysts to understand the structure, quality, and underlying patterns within a dataset before applying formal modelling techniques. By combining statistical methods and visualisation, EDA supports more informed, accurate, and reliable analysis outcomes.

Technique

Identifying and Mitigating Data Quality Risks

Data quality risks threaten the reliability of analysis by introducing errors, gaps, or inconsistencies into datasets. This technique outlines how analysts can identify and mitigate these risks using structured techniques, effective escalation, and data governance practices (Wang and Strong, 1996; Ilyas and Chu, 2019).

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